US8812258B2 - Identifying a type of motion of an object - Google Patents
Identifying a type of motion of an object Download PDFInfo
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- US8812258B2 US8812258B2 US13/975,170 US201313975170A US8812258B2 US 8812258 B2 US8812258 B2 US 8812258B2 US 201313975170 A US201313975170 A US 201313975170A US 8812258 B2 US8812258 B2 US 8812258B2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
- G01C22/006—Pedometers
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
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- G06F19/345—
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- G06K9/00342—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
Definitions
- the described embodiments relate generally to motion detecting. More particularly, the described embodiments relate to a method and apparatus for identifying a type of motion of an animate or inanimate object.
- An embodiment includes a method of detecting human condition and activity of a user.
- the method includes generating, by a motion detection device that includes a motion detection sensor, an acceleration signature based on sensed acceleration of an object that represents motion of the user, the motion detection device determining what network connections are available to the motion detection device, matching the acceleration signature with at least one of a plurality of stored acceleration signatures of motions of human beings, wherein each stored acceleration signatures corresponds with a type of motion, wherein the motion detection device distributes at least some of the acceleration signature matching processing when processing capability is available to the motion detection device though available network connections, and identifying a type of motion of the user and identifying a condition of the user based on the matching of the acceleration signature with a stored acceleration signature.
- the apparatus includes a motion detection sensor operative to generate an acceleration signature based on sensed acceleration of the user, and a controller.
- the controller is operative to determine what network connections are available to the motion detection device, match the acceleration signature with at least one of a plurality of stored acceleration signatures, wherein each stored acceleration signatures corresponds with a type of motion of the user, wherein the apparatus distributes at least some of the acceleration signature matching processing when processing capability is available to the motion detection device though available network connections, and identify the type of motion of the user and identify a condition of the user based on the matching of the acceleration signature.
- FIG. 1 shows examples of different types of motions of a human being that an object attached to the human being can be used to detect or sense.
- FIGS. 2A , 2 B, 2 C shows examples of time-lines of several different acceleration curves (signatures), wherein each signature is associated with a different type of sensed or detected motion.
- FIG. 3 is an example of a block diagram of a motion detection device.
- FIG. 4 is a flowchart that includes the steps of an example of a method for detecting various motions of daily living activities and emergency situations, such as, a fall.
- FIG. 5 is a flowchart that includes the steps of a method for detection of a fall.
- FIG. 6 is a flow chart that includes the steps of one example of a method of identifying a type of motion of an animate or inanimate object.
- FIG. 7 is a flow chart that includes steps of one example of a method of a motion detection device checking network availability for improvements in speed and/or processing power of acceleration signature matching.
- FIG. 8 shows a motion detection device that can be connected to one of multiple networks.
- the atomic movements become simply alphabets that include elemental motions.
- specific sequences of elemental motions become the vocabulary that comprises human behavior.
- the behavioral pattern of the person is walking to the door or the house, opening and closing the door, walking further to the car, settling down in the car, starting the engine, accelerating the car, going through a series of stops, starts and turns, parking the car, getting out and closing the car door, and finally walking to the shopping center.
- This sequence of human behavior is comprised of individual motions such as standing, walking, sitting, accelerating (in the car), decelerating, and turning left or right.
- Each individual motion, for example walking is comprised of multiple atomic movements such as acceleration in an upward direction, acceleration in a downward direction, a modest forward acceleration with each step, a modest deceleration with each step, and so on.
- Motion is identified by generating acceleration signatures based on the sensed acceleration of the object.
- the acceleration signatures are compared with a library of motion signature, allowing the motion of the object to be identified. Further, sequences of the motions can be determined, allowing identification of activities of, for example, a person the object is attached to.
- Algorithms used for pattern recognition should have the sophistication to accurately handle a wide range of motions. Such algorithms should have the ability to recognize the identical characteristics of a particular motion by a given human being, yet allow for minor variations arising from human randomness. Additionally, the devices used to monitor peoples' movement need to be miniature and easy to wear. These two objectives are fundamentally opposed. However, the described embodiments provide a single cohesive system that is both sophisticated enough to detect a wide range of motions.
- FIG. 1 shows examples of different types of motions of a human being that an object attached to the human being can be used to detect or sense.
- the human motions can include, for example, standing, sleeping, walking, and running A first motion 110 can include walking A second motion 120 can include falling.
- a third motion 130 can include running Each of the motions generates a unique motion signature.
- the signatures can be universal to, for example, many individuals. Additionally, the signatures can have additional characteristics that are unique to an individual.
- FIGS. 2A , 2 B, 2 C shows examples of different types of acceleration and orientation signatures for various sample motions by human beings. It should be noted that these signatures are expected to have certain components that are common from one human being to the next, but also have certain components that vary from one human to the next.
- FIGS. 2A , 2 B, 2 C are depicted in only one orientation. That is, three accelerometers can be used to generate acceleration signatures in the X, Y and Z (three) orientations.
- the signatures of FIGS. 2A , 2 B, 2 C only show the signature of one of the three orientations. It is to be understood that matching can use the other orientations as well.
- FIG. 2A shows an example of an acceleration signature of a person doing a slow fall and lying down summersault.
- FIG. 2B shows an example of an acceleration signature of a person slipping and falling back on a bouncy surface (for example, an air mattress).
- FIG. 2C shows an acceleration signature of a person fall on their face with their knees flexed.
- FIG. 3 is an example of a block diagram of a motion detection device.
- the motion detection device can be attached to an object, and therefore, detect motion of the object that can be identified. Based on the identified motion, estimates of the behavior and conditions of the object can be determined.
- the motion detection device includes sensors (such as, accelerometers) that detect motion of the object.
- sensors such as, accelerometers
- One embodiment of the sensors includes accelerometers 312 , 314 , 316 that can sense, for example, acceleration of the object in X, Y and Z directional orientations. It is to be understood that other types of motion detection sensors can alternatively be used.
- An analog to digital converter digitizes analog accelerometer signals.
- the digitized signals are received by compare processing circuitry 330 that compares the digitized accelerometer signals with signatures that have been stored within a library of signatures 340 .
- Each signature corresponds with a type of motion. Therefore, when a match between the digitized accelerometer signals and a signature stored in the library 340 , the type of motion experienced by the motion detection device can determined.
- An embodiment includes filtering the accelerometer signals before attempting to match the signatures. Additionally, the matching process can be made simpler by reducing the possible signature matches.
- An embodiment includes identifying a previous human activity context. That is, for example, by knowing that the previous human activity was walking, certain signatures can intelligently be eliminated from the possible matches of the present activity that occurs subsequent to the previous human activity (walking).
- An embodiment includes additionally reducing the number of possible signature matches by performing a time-domain analysis on the accelerometer signal.
- the time-domain analysis can be used to identify a transient or steady-state signature of the accelerometer signal. That is, for example, a walk may have a prominent steady-state signature, whereas a fall may have a prominent transient signature.
- Identification of the transient or steady-state signature of the accelerometer signal can further reduce or eliminate the number of possible signature matches, and therefore, make the task of matching the accelerometer signature with a signature within the library of signature simpler, and easier to accomplish. More specifically, the required signal processing is simpler, easier, and requires less computing power.
- an audio device 360 and/or a global positioning system (GPS) 370 can engaged to provide additional information that can be used to determine the situation of, for example, a human being the motion detection device is attached to.
- GPS global positioning system
- the condition, or information relating to the motion detection device can be communicated through a wired or wireless connection.
- a receiver of the information can process it, and make a determination regarding the status of the human being the motion detection device is attached to.
- FIG. 4 is a flowchart that includes the steps of an example of a method for detecting various motions of daily living activities and emergency situations, such as, a fall.
- a first step 410 includes monitoring an activity of a person the motion detection device is attached.
- Raw signal data is collected from, for example, an accelerometer sensor.
- a second step 420 includes performing instantaneous computations over raw signals to compute atomic motions along with gravity vector and tilt vector.
- a step third step 430 includes applying series of digital filters to remove noise in the atomic motions data.
- a fourth step 440 includes performing state analysis on series of atomic data samples and forming context. Depending on the state analysis, the series of atomic data is passed through either a step 445 periodic or steady state data analysis or a step 450 transient state data analysis.
- a sixth step 460 includes formation of macro motion signatures.
- the macro motion signatures are built from an output of state analysis vectors using known wavelet transformation techniques (for example, a Haar Transform).
- the transform performs pattern matching on current motion pattern with existing motion pattern library using, for example, DWT (Discreet Wavelet Transform) techniques.
- Complex motion wavelets are later matched using statistical pattern matching techniques, such as, HHMM (Hidden Heuristic Markov Model).
- the statistical pattern matching includes detecting and classifying events of interest.
- the events of interest are built by observing various motions and orientation states data of an animate or inanimate object. This data is used to train the statistical model which performs the motion/activity detection. Each activity will have its own model trained based on the observed data.
- a seventh step 470 includes a learning system providing the right model for the user from a set of model. It also aids in building newer (personal) patterns which are not in the library for the person who is wearing the motion detection device.
- An eighth step 480 includes pre-building a motion database of motion libraries against which motion signatures are compared. The database adds new motion/states signature dynamically as they are identified.
- FIG. 5 is a flowchart that includes the steps of an example of a method for detecting a fall.
- a first step 510 includes monitoring an activity of, for example, a person the motion detection device is attached to.
- a step 515 includes recording and reporting in deviations in normal motion patterns of the person.
- a step 520 includes detecting the acceleration magnitude deviation exceeding a threshold. The acceleration magnitude deviation exceeding the threshold can be sensed as a probable fall, and audio recording is initiated. Upon detection of this condition, sound recording of the person the motion detection device is connected to can be activated. The activation of sound can provide additional information that can be useful in assessing the situation of the person.
- a step 530 includes monitoring the person after the probable fall.
- a step 525 includes detection of another acceleration having magnitude lesser than the threshold, and continuing monitoring of audio.
- a step 535 includes detecting a short period of inactivity.
- a step 540 includes monitoring the person after determining a fall probably occurred.
- a step 545 includes subsequently detecting normal types of motion and turning off the audio because the person seems to be performing normal activity.
- a step 550 includes monitoring a period of inactivity.
- a step 555 includes additional analysis of detected information and signals.
- a step 560 includes further analysis including motion data, orientation detection all indicating the person is functioning normally.
- a step 560 includes determining that a fall has occurred based on the analysis of the motion data, and analysis of a concluded end position and orientation of the person. The sound recording can be de-activated.
- a step 565 includes concluding that a fall has occurred.
- a step 570 includes sending an alert and reporting sound recordings.
- a step 575 includes the fall having been reported.
- a step 580 includes an acknowledgement of the fall.
- FIG. 6 is a flow chart that includes the steps of one example of a method of identifying a type of motion of an animate or inanimate object.
- a first step 610 includes generating an acceleration signature (for example, a tri-axial) based on the sensed acceleration of the object.
- a second step 620 includes matching the acceleration signature with at least one of a plurality of stored acceleration signatures, wherein each stored acceleration signatures corresponds with type of motion.
- a third step 630 includes identifying the type of motion of the object based on the statistical (pattern) matching or exact matching of the acceleration signature.
- the acceleration signal can be created using a wavelet transformation.
- the type of motion includes at least one of atomic motion, elemental motion and macro-motion.
- the first step 610 can include generating an acceleration signature, (and/or) orientation and audio signature based on the sensed acceleration, orientation of the object and audio generated by the object, for example, a thud of a fall, or a cry for help.
- Atomic motion includes but is not limited to a sharp jolt, a gentle acceleration, complete stillness, a light acceleration that becomes stronger, a strong acceleration that fades, a sinusoidal or quasi-sinusoidal acceleration pattern, vehicular acceleration, vehicular deceleration, vehicular left and right turns, and more.
- Elemental motion includes but is not limited to motion patterns for walking, running, fitness motions (e.g. elliptical machine exercises, rowing, stair climbing, aerobics, skipping rope, bicycling . . . ), vehicular traversal, sleeping, sitting, crawling, turning over in bed, getting out of bed, getting up from chair, and more.
- fitness motions e.g. elliptical machine exercises, rowing, stair climbing, aerobics, skipping rope, bicycling . . .
- vehicular traversal e.g. elliptical machine exercises, rowing, stair climbing, aerobics, skipping rope, bicycling . . .
- vehicular traversal sleeping, sitting, crawling, turning over in bed, getting out of bed, getting up from chair, and more.
- Macro-motion includes but is not limited to going for a walk in the park, leaving home and driving to the shopping center, getting out of bed and visiting the bathroom, performing household chores, playing a game of tennis, and more.
- Each of the plurality of stored acceleration signatures corresponds with a particular type of motion.
- An embodiment includes a common library and a specific library, and matching the acceleration signature includes matching the acceleration signature with stored acceleration signatures of the common library, and then matching the acceleration signature with stored acceleration signatures of the specific library.
- the general library includes universal acceleration signatures
- the specific library includes personal acceleration signatures. That is, for example, the stored acceleration signatures of the common library are useable for matching acceleration signatures of motions of multiple humans, and the stored acceleration signatures of the specific library are useable for matching acceleration signatures of motions of a particular human.
- each library can be further categorized to reduce the number of possible matches. For example, at an initialization, a user may enter physical characteristics of the user, such as, age, sex and/or physical characteristics (such as, the user has a limp). Thereby, the possible signatures matches within the general library can be reduced.
- the signature entries within the specific library can be learned (built) over time as the human wearing the motion detection device goes through normal activities of the specific human.
- the specific library can be added to, and improved over time.
- An embodiment includes filtering the acceleration signals. Additional embodiment include reducing the number of stored acceleration signature matches by identifying a previous activity of the object, and performing a time domain analysis on the filtered acceleration signal to identify transient signatures or steady-state signatures of the filtered acceleration signal. That is, by identifying a previous activity (for example, a human walking of sleeping) the possible number of present activities can be reduced, and therefore, the number of possible stored acceleration signature matches reduced. Additionally, the transient and/or steady-state signatures can be used to reduce the number of possible stored acceleration signature matches, which can improve the processing speed.
- Another embodiment includes activating audio sensing of the object if matches are made with at least portions of particular stored acceleration signatures. For example, if the acceleration signature exceeds a threshold value, then audio sensing of the object is activated. This is useful because the audio information can provide additional clues as to what, for example, the condition of a person. That is, a fall may be detected, and audio information can be used to confirm that a fall has in fact occurred.
- Another embodiment includes transmitting the sensed audio. For example, of a user wearing the object has fallen, and the fall has been detected, audio information can be very useful for determining the condition of the user.
- the audio information can allow a receiver of the audio information to determine, for example, if the user is in pain, unconscious or in a dangerous situation (for example, in a shower or in a fire).
- An embodiment includes the object being associated a person, and the stored acceleration signatures corresponding with different types of motion related to the person.
- a particular embodiment includes identifying an activity of the person based on a sequence of identified motions of the person.
- the activity of the person can include, for example, falling (the most important in some applications), walking, running, driving and more.
- the activities can be classified as daily living activities such as walking, running, sitting, sleeping, driving, climbing stairs, and more, or sporadic activities, such as falling, having a car collision, having a seizure and so on.
- An embodiment includes transmitting information related to the identified type of motion if matches are made with particular stored acceleration signatures.
- the information related to the identified type of motion can include at least one of motions associated with a person the object is associated with.
- the motions can include, for example, a heartbeat of the person, muscular spasms, facial twitches, involuntary reflex movements which can be sensed by, for example, an accelerometer.
- the information related to the identified type of motion can include at least one of location of the object, audio sensed by the object, temperature of the object.
- Another embodiment includes storing at least one of the plurality of stored acceleration signatures during an initialization cycle.
- the initializing cycle can be influenced based on what the object is attached to. That is, initializing the stored acceleration signatures (motion patterns) can be based on what the object is attached to, which can both reduce the number of signatures required to be stored within, for example, the general library, and reduce the number of possible matches and reduce the processing required to identify a match.
- initializing the stored acceleration signatures can be based on who the object is attached to, which can influence the specific library.
- the initialization can be used to determine motions unique, for example, to an individual. For example, a unique motion can be identified for a person who walks with a limp, and the device can be initialized with motion patterns of the person walking with a limp.
- An embodiment includes initiating a low-power sleep mode of the object if sensed acceleration is below a threshold for a predetermined amount of time. That is, if, for example, a person is sensed to be sleeping, power can be saved by de-activating at least a portion of the motion sensing device.
- FIG. 7 is a flow chart that includes steps of one example of a method of a motion detection device checking network availability for improvements in speed and/or processing power of acceleration signature matching, wherein the motion detection device includes motion detection sensors that generate the acceleration signal.
- a first step 710 includes the motion detection device determining what network connections are available to the motion detection device.
- a second step 710 includes the motion detection device distributing at least some of the acceleration signature matching processing if processing capability is available to the motion detection device though available network connections.
- the motion detection device distributes the acceleration signature matching processing if the processing capability is available to the motion detection device though available network connections, and distributing the acceleration signature matching processing saves the motion detection device processing power.
- the motion detection device distributes the acceleration signature matching processing if the processing capability is available to the motion detection device though available network connections, and distributing the acceleration signature matching processing increases a speed of the motion detection device processing.
- the motion detection device distributes the processing to optimize both power and processing speed. Additionally, the processing distribution can be dependent upon the bandwidths of the available network connections. That is, some networks connections can generally support higher data transfer rates, and therefore, influence the processing speed.
- the motion detection device scales its processing to the level of processing available. That is, as additional processing power becomes available to the motion detection device, the motion detection device can increase the complexity of the signature matching processing.
- the processing can be distributed as processing capability becomes available through network connections. The processing can be performed in different locations as network connectivity becomes available, which can advantageously reduce the power consumption of the motion detection device and/or increase the speed of the processing.
- FIG. 8 shows a motion detection device 300 that can be connected to one of multiple networks.
- Examples of possible networks the motion detection device 300 can connect to, include a cellular network 820 through, for example, a blue tooth wireless link 810 , or to a home base station 840 through, for example, a Zigbee wireless link 845 .
- the wireless links 810 , 845 can each provide different levels of bandwidth.
- Each of the networks includes available processing capabilities 830 , 850 .
- the motion detection device 300 If the motion detection device 300 does not have any network connections available, the motion detection device 300 must perform its own matching processing. If this is the case, then the processing algorithms may be less complex to reduce processing power, and/or reduce processing speed. For example, the matching processing can be made simpler by comparing threshold levels for elemental motions by extracting significant wavelet coefficients. Acceleration signals data acquisition is performed in chunk of processing every few mili-seconds by waking up. For all other times the processor rests in low-power mode. Except for the emergency situation, the RF communication is done periodically when the data is in steady state, there is no need to send it to network i.e. when the object is in sedentary there is no need to send data change in the state is communicated to network.
- the operation of the motion detection device 300 may be altered. For example, if the motion detection device 300 detects an emergency situation (such as, a fall), the motion detection device 300 may generate an audio alert. If a network connection was available, the audio alert may not be generated, but an alert may be transmitted over the available network.
- an emergency situation such as, a fall
- the motion detection device 300 includes a processor in which at least a portion of the analysis and signature matching can processing can be completed. However, if the motion detection device 300 has one or more networks available to the motion detection device 300 , the motion detection device can off-load some of the processing to one of the processors 730 , 750 associated with the networks.
- the determination of whether to off-load the processing can be based on both the processing capabilities provided by available networks, and the data rates (bandwidth) provided by each of the available networks.
Abstract
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US20110066383A1 (en) | 2011-03-17 |
WO2012036958A2 (en) | 2012-03-22 |
US20130346014A1 (en) | 2013-12-26 |
WO2012036958A3 (en) | 2012-07-12 |
US8560267B2 (en) | 2013-10-15 |
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